Universal MelGAN: A Robust Neural Vocoder for High-Fidelity Waveform Generation in Multiple Domains

• URL: http://arxiv.org/abs/2011.09631v2
• Date: Thu, 4 Mar 2021 02:00:12 GMT
• Title: Universal MelGAN: A Robust Neural Vocoder for High-Fidelity Waveform Generation in Multiple Domains
• Authors: Won Jang, Dan Lim, Jaesam Yoon
• Abstract summary: We propose Universal MelGAN, a vocoder that synthesizes high-fidelity speech in multiple domains. MelGAN-based structure is trained with a dataset of hundreds of speakers. We added multi-resolution spectrogram discriminators to sharpen the spectral resolution of the generated waveforms.
• Score: 1.8047694351309207
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We propose Universal MelGAN, a vocoder that synthesizes high-fidelity speech in multiple domains. To preserve sound quality when the MelGAN-based structure is trained with a dataset of hundreds of speakers, we added multi-resolution spectrogram discriminators to sharpen the spectral resolution of the generated waveforms. This enables the model to generate realistic waveforms of multi-speakers, by alleviating the over-smoothing problem in the high frequency band of the large footprint model. Our structure generates signals close to ground-truth data without reducing the inference speed, by discriminating the waveform and spectrogram during training. The model achieved the best mean opinion score (MOS) in most scenarios using ground-truth mel-spectrogram as an input. Especially, it showed superior performance in unseen domains with regard of speaker, emotion, and language. Moreover, in a multi-speaker text-to-speech scenario using mel-spectrogram generated by a transformer model, it synthesized high-fidelity speech of 4.22 MOS. These results, achieved without external domain information, highlight the potential of the proposed model as a universal vocoder.

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